Canopy gap detection in
three enlarged subareas (white rectangles).
This repository contains code for forest canopy gap detection using a deep learning model trained on gaps automatically generated from airborne laser scanning (ALS)-derived canopy height models (CHMs), combined with spectral (true digital orthophotos) and height information from digital aerial photogrammetry (DAP)-based CHMs. For further details, see the following paper:
Franz, F., Seidel, D., Beckschäfer, P., 2025. Deep learning-based canopy gap detection using a cross-technological approach with airborne laser scanning and aerial imagery data. Ecol. Informatics.
…
@article{franz2025deep,
title={Deep learning-based canopy gap detection using a cross-technological approach with airborne laser scanning and aerial imagery data},
author={Franz, Florian and Seidel, Dominik and Becksch{\"a}fer, Philip},
journal={Ecological Informatics},
pages={},
year={2025},
publisher={Elsevier}
}